Fifteen years ago, Webvan spectacularly failed to bring grocery delivery online. Speculation has been high that the current wave of on-demand grocery delivery startups will meet similar fates. Jeremy Stanley explains why this time the story will be different—data science is the key. Innovations in mobile applications have paved the way, but significant investments in algorithms to optimize efficiency will drive positive unit economics.
Jeremy explores how Instacart has used data science to optimize last-mile delivery and balance supply and demand to drive big gains in efficiency that are transforming unit economics in this competitive space. He describes how improvements in predicting outcomes, batching algorithms, forecasting, and staffing have contributed to big improvements in delivery efficiency and outlines some of the multiple competing objectives Instacart is optimizing for in order to provide a great customer and shopper experience. Jeremy ends by talking about how data science is organized at Instacart and how it collaborates with product, engineering, and field operators to make rapid innovation possible in a complex ecosystem.
Jeremy is currently the VP of data science at Instacart, where he works closely with data scientists who are integrated into product teams to drive growth and profitability through logistics, catalog, search, consumer, shopper, and partner applications. Previously, Jeremy was chief data scientist and EVP of engineering at Sailthru, which builds data-driven solutions for marketers to drive long-term customer engagement and optimize revenue opportunities. As chief data scientist, he was responsible for the intelligence in the marketing personalization platform, which included prediction, recommendation, and optimization algorithms. As EVP of engineering, Jeremy led development, operations, database, and engineering support teams and partnered with the CTO to drive innovation and stability while scaling.
Earlier in his career, Jeremy was the CTO of Collective, where he led a team of product managers, engineers, and data scientists in creating technology platforms that used machine learning and big data to address challenging multiscreen advertising problems, and he founded and led the Global Markets Analytics group at Ernst & Young (EY), which analyzed the firm’s markets, financial and personnel data to inform executive decision making. His background in data-driven technology products spans a decade consulting with numerous global financial services firms on predictive modeling applications as a leader in the Customer Analytics Advisory practice at EY.
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